

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					lowerCAmelCase_			      =      'platform'

					import jax
					import jax.numpy as jnp

					from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
					    FlaxBlenderbotSmallForConditionalGeneration,
					    FlaxBlenderbotSmallModel,
					    shift_tokens_right,
					)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						):





						if attention_mask is None:
												snake_case_  =      np.where(input_ids != config.pad_token_id    ,						1    ,						0			)
						if decoder_attention_mask is None:
												snake_case_  =      np.where(decoder_input_ids != config.pad_token_id    ,						1    ,						0			)
						if head_mask is None:
												snake_case_  =      np.ones((config.encoder_layers, config.encoder_attention_heads)			)
						if decoder_head_mask is None:
												snake_case_  =      np.ones((config.decoder_layers, config.decoder_attention_heads)			)
						if cross_attn_head_mask is None:
												snake_case_  =      np.ones((config.decoder_layers, config.decoder_attention_heads)			)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": attention_mask,
						}





class       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[Any]=1_3				,  _UpperCamelCase					:							int=7				,  _UpperCamelCase					:							int=True				,  _UpperCamelCase					:							Tuple=False				,  _UpperCamelCase					:							Dict=9_9				,  _UpperCamelCase					:							Optional[int]=1_6				,  _UpperCamelCase					:							Optional[Any]=2				,  _UpperCamelCase					:							str=4				,  _UpperCamelCase					:							int=4				,  _UpperCamelCase					:							int="gelu"				,  _UpperCamelCase					:							List[Any]=0.1				,  _UpperCamelCase					:							int=0.1				,  _UpperCamelCase					:							Optional[int]=3_2				,  _UpperCamelCase					:							str=2				,  _UpperCamelCase					:							List[str]=1				,  _UpperCamelCase					:							str=0				,  _UpperCamelCase					:							Optional[Any]=0.02				,  ) ->Any:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      seq_length
									snake_case_  =      is_training
									snake_case_  =      use_labels
									snake_case_  =      vocab_size
									snake_case_  =      hidden_size
									snake_case_  =      num_hidden_layers
									snake_case_  =      num_attention_heads
									snake_case_  =      intermediate_size
									snake_case_  =      hidden_act
									snake_case_  =      hidden_dropout_prob
									snake_case_  =      attention_probs_dropout_prob
									snake_case_  =      max_position_embeddings
									snake_case_  =      eos_token_id
									snake_case_  =      pad_token_id
									snake_case_  =      bos_token_id
									snake_case_  =      initializer_range







			def 							snake_case__(	self					:							Any				) ->Tuple:
									snake_case_  =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]				,  self.vocab_size				)				,  3				,  self.vocab_size				)
									snake_case_  =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)				,  dtype=np.intaa				))				,  -1				)

									snake_case_  =      shift_tokens_right(__snake_case				,  1				,  2				)

									snake_case_  =      BlenderbotSmallConfig(
									    vocab_size=self.vocab_size				,  d_model=self.hidden_size				,  encoder_layers=self.num_hidden_layers				,  decoder_layers=self.num_hidden_layers				,  encoder_attention_heads=self.num_attention_heads				,  decoder_attention_heads=self.num_attention_heads				,  encoder_ffn_dim=self.intermediate_size				,  decoder_ffn_dim=self.intermediate_size				,  dropout=self.hidden_dropout_prob				,  attention_dropout=self.attention_probs_dropout_prob				,  max_position_embeddings=self.max_position_embeddings				,  eos_token_id=self.eos_token_id				,  bos_token_id=self.bos_token_id				,  pad_token_id=self.pad_token_id				,  initializer_range=self.initializer_range				,  use_cache=__snake_case				,  )
									snake_case_  =      prepare_blenderbot_inputs_dict(__snake_case				,  __snake_case				,  __snake_case				)
									return config, inputs_dict







			def 							snake_case__(	self					:							Dict				) ->Any:
									snake_case_  =      self.prepare_config_and_inputs()
									return config, inputs_dict







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							List[Any]				) ->int:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(__snake_case				)

									snake_case_  =      model.encode(inputs_dict['''input_ids''']				)

									snake_case_  =      (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									snake_case_  =      model.init_cache(decoder_input_ids.shape[0]				,  __snake_case				,  __snake_case				)
									snake_case_  =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,  dtype='''i4'''				)

									snake_case_  =      jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,  )
									snake_case_  =      model.decode(
									    decoder_input_ids[:, :-1]				,  __snake_case				,  decoder_attention_mask=__snake_case				,  past_key_values=__snake_case				,  decoder_position_ids=__snake_case				,  )

									snake_case_  =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,  dtype='''i4'''				)
									snake_case_  =      model.decode(
									    decoder_input_ids[:, -1:]				,  __snake_case				,  decoder_attention_mask=__snake_case				,  past_key_values=outputs_cache.past_key_values				,  decoder_position_ids=__snake_case				,  )

									snake_case_  =      model.decode(__snake_case				,  __snake_case				)

									snake_case_  =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3				,  msg=f'''Max diff is {diff}'''				)







			def 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							str				) ->Any:
									snake_case_  =      2_0
									snake_case_  =      model_class_name(__snake_case				)

									snake_case_  =      model.encode(inputs_dict['''input_ids''']				)

									snake_case_  =      (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									snake_case_  =      jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
									    ]				,  axis=-1				,  )

									snake_case_  =      model.init_cache(decoder_input_ids.shape[0]				,  __snake_case				,  __snake_case				)
									snake_case_  =      jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]				,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,  )

									snake_case_  =      model.decode(
									    decoder_input_ids[:, :-1]				,  __snake_case				,  decoder_attention_mask=__snake_case				,  past_key_values=__snake_case				,  decoder_position_ids=__snake_case				,  )
									snake_case_  =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,  dtype='''i4'''				)
									snake_case_  =      model.decode(
									    decoder_input_ids[:, -1:]				,  __snake_case				,  past_key_values=outputs_cache.past_key_values				,  decoder_attention_mask=__snake_case				,  decoder_position_ids=__snake_case				,  )

									snake_case_  =      model.decode(__snake_case				,  __snake_case				,  decoder_attention_mask=__snake_case				)

									snake_case_  =      np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
									self.parent.assertTrue(diff < 1e-3				,  msg=f'''Max diff is {diff}'''				)






@require_flax
class       snake_case_  (						unittest.TestCase   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  str		     =							99







			def 							snake_case__(	self					:							Optional[int]				) ->Optional[int]:
									snake_case_  =      np.array(
									    [
									        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
									        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
									        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
									        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
									        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
									        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
									        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
									        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
									        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
									        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
									        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
									        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
									        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
									    ]				,  dtype=np.intaa				,  )

									snake_case_  =      input_ids.shape[0]
									snake_case_  =      BlenderbotSmallConfig(
									    vocab_size=self.vocab_size				,  d_model=2_4				,  encoder_layers=2				,  decoder_layers=2				,  encoder_attention_heads=2				,  decoder_attention_heads=2				,  encoder_ffn_dim=3_2				,  decoder_ffn_dim=3_2				,  max_position_embeddings=4_8				,  eos_token_id=2				,  pad_token_id=1				,  bos_token_id=0				,  )
									return config, input_ids, batch_size







			def 							snake_case__(	self					:							List[str]				) ->Optional[int]:
									snake_case_  =      self._get_config_and_data()
									snake_case_  =      FlaxBlenderbotSmallForConditionalGeneration(__snake_case				)
									snake_case_  =      lm_model(input_ids=__snake_case				)
									snake_case_  =      (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape				,  __snake_case				)







			def 							snake_case__(	self					:							List[str]				) ->Optional[int]:
									snake_case_  =      BlenderbotSmallConfig(
									    vocab_size=self.vocab_size				,  d_model=1_4				,  encoder_layers=2				,  decoder_layers=2				,  encoder_attention_heads=2				,  decoder_attention_heads=2				,  encoder_ffn_dim=8				,  decoder_ffn_dim=8				,  max_position_embeddings=4_8				,  )
									snake_case_  =      FlaxBlenderbotSmallForConditionalGeneration(__snake_case				)
									snake_case_  =      np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]				,  dtype=np.intaa				)
									snake_case_  =      np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]				,  dtype=np.intaa				)
									snake_case_  =      lm_model(input_ids=__snake_case				,  decoder_input_ids=__snake_case				)
									snake_case_  =      (*summary.shape, config.vocab_size)
									self.assertEqual(outputs['''logits'''].shape				,  __snake_case				)







			def 							snake_case__(	self					:							str				) ->List[str]:
									snake_case_  =      np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]				,  dtype=np.intaa				)
									snake_case_  =      shift_tokens_right(__snake_case				,  1				,  2				)
									snake_case_  =      np.equal(__snake_case				,  1				).astype(np.floataa				).sum()
									snake_case_  =      np.equal(__snake_case				,  1				).astype(np.floataa				).sum()
									self.assertEqual(shifted.shape				,  input_ids.shape				)
									self.assertEqual(__snake_case				,  n_pad_before - 1				)
									self.assertTrue(np.equal(shifted[:, 0]				,  2				).all()				)






@require_flax
class       snake_case_  (						_snake_case      ,			unittest.TestCase      ,			_snake_case   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Dict		     =							True
			SCREAMING_SNAKE_CASE	:  List[Any]		     =							(
			    (
			        FlaxBlenderbotSmallModel,
			        FlaxBlenderbotSmallForConditionalGeneration,
			    )
			    if is_flax_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE	:  List[str]		     =							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()







			def 							snake_case__(	self					:							Any				) ->int:
									snake_case_  =      FlaxBlenderbotSmallModelTester(self				)







			def 							snake_case__(	self					:							int				) ->Any:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(__snake_case				,  __snake_case				,  __snake_case				)







			def 							snake_case__(	self					:							Tuple				) ->Optional[Any]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward_with_attn_mask(__snake_case				,  __snake_case				,  __snake_case				)







			def 							snake_case__(	self					:							str				) ->Optional[int]:
									snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					snake_case_  =      self._prepare_for_class(__snake_case				,  __snake_case				)
																					snake_case_  =      model_class(__snake_case				)

																					@jax.jit
																					def encode_jitted(_UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							List[str]=None				,  **_UpperCamelCase					:							Dict				):
																											return model.encode(input_ids=__snake_case				,  attention_mask=__snake_case				)

																					with self.subTest('''JIT Enabled'''				):
																											snake_case_  =      encode_jitted(**__snake_case				).to_tuple()

																					with self.subTest('''JIT Disabled'''				):
																											with jax.disable_jit():
																																	snake_case_  =      encode_jitted(**__snake_case				).to_tuple()

																					self.assertEqual(len(__snake_case				)				,  len(__snake_case				)				)
																					for jitted_output, output in zip(__snake_case				,  __snake_case				):
																											self.assertEqual(jitted_output.shape				,  output.shape				)







			def 							snake_case__(	self					:							int				) ->Dict:
									snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__				):
																					snake_case_  =      model_class(__snake_case				)
																					snake_case_  =      model.encode(inputs_dict['''input_ids''']				,  inputs_dict['''attention_mask''']				)

																					snake_case_  =      {
																					    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																					    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																					    """encoder_outputs""": encoder_outputs,
																					}

																					@jax.jit
																					def decode_jitted(_UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Optional[Any]				):
																											return model.decode(
																											    decoder_input_ids=__snake_case				,  decoder_attention_mask=__snake_case				,  encoder_outputs=__snake_case				,  )

																					with self.subTest('''JIT Enabled'''				):
																											snake_case_  =      decode_jitted(**__snake_case				).to_tuple()

																					with self.subTest('''JIT Disabled'''				):
																											with jax.disable_jit():
																																	snake_case_  =      decode_jitted(**__snake_case				).to_tuple()

																					self.assertEqual(len(__snake_case				)				,  len(__snake_case				)				)
																					for jitted_output, output in zip(__snake_case				,  __snake_case				):
																											self.assertEqual(jitted_output.shape				,  output.shape				)







			@slow
			def 							snake_case__(	self					:							Dict				) ->int:
									for model_class_name in self.all_model_classes:
															snake_case_  =      model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''				)
															# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
															snake_case_  =      np.ones((1, 1)				) * model.config.eos_token_id
															snake_case_  =      model(__snake_case				)
															self.assertIsNotNone(__snake_case				)



'''simple docstring'''


import numpy as np





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= (0, 0)
											_SCREAMING_SNAKE_CASE					:			List[str]							= None
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
											_SCREAMING_SNAKE_CASE					:			List[str]							= 0
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0






							def __eq__(       self       ,   __snake_case    ):
											return self.position == cell.position






							def UpperCAmelCase_      (       self    ):
											print(self.position    )





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case=(5, 5)    ):
											_SCREAMING_SNAKE_CASE					:			str							= np.zeros(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= world_size[0]
											_SCREAMING_SNAKE_CASE					:			Tuple							= world_size[1]






							def UpperCAmelCase_      (       self    ):
											print(self.w    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[1]
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for n in neughbour_cord:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= current_x + n[0]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= current_y + n[1]
															if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= Cell()
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (x, y)
																			_SCREAMING_SNAKE_CASE					:			Dict							= cell
																			neighbours.append(__snake_case    )
											return neighbours







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				_open.append(SCREAMING_SNAKE_CASE__	)

				while _open:
								_SCREAMING_SNAKE_CASE					:			Any							= np.argmin([n.f for n in _open]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= _open[min_f]
								_closed.append(_open.pop(SCREAMING_SNAKE_CASE__	)	)
								if current == goal:
												break
								for n in world.get_neigbours(SCREAMING_SNAKE_CASE__	):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= current.g + 1
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= n.position
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= goal.position
												_SCREAMING_SNAKE_CASE					:			Dict							= (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE					:			Any							= n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= []
				while current.parent is not None:
								path.append(current.position	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= current.parent
				path.append(current.position	)
				return path[::-1]


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						Gridworld()
				# Start position and goal
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(0, 0)
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(4, 4)
				print(F"path from {start.position} to {goal.position}")
				UpperCAmelCase_						:  Union[str, Any]				  =						astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCAmelCase_						:  Optional[int]				  =						1
				print(world.w)




